Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …

A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …

Federated learning (FL) enables many data owners (eg, mobile devices) to train a joint ML
model (eg, a next-word prediction classifier) without the need of sharing their private training …

We address the problem of Federated Learning (FL) where users are distributed and
partitioned into clusters. This setup captures settings where different groups of users have …

Federated learning (FL) is a well established method for performing machine learning tasks
over massively distributed data. However in settings where data is distributed in a non-iid …

Federated learning (FL) is currently the most widely adopted framework for collaborative
training of (deep) machine learning models under privacy constraints. Albeit its popularity, it …

Federated learning is a distributed framework according to which a model is trained over a
set of devices, while kee** data localized. This framework faces several systems-oriented …